1. David Roodman (2008) Presentation by Faraharivony Rakotomamonjy and Estelle Zemmour

2. Outline Introduction Literature review Empirical strategy Specifications issues on the aid-growth literature Conclusion

3. Introduction Originality of this paper: -use of non-instrumental techniques to examine the nature of endogeneity in aid- growth literature (sign-strength-causality) -discussion on a number of common specification problems He concludes the relationship goes to the looking-glass : while AEL supports a positive relationship from aid to growth, growth appears to negatively Granger-cause aid.

4. Literature review (1) Up to the late 1990s : attempts to provide evidences of the overall effect of aid on growth=> no consensus Structural break in AEL with Burnside and Dollar (2000): analyze the conditionality of aid effectiveness

5. Literature review (2) This coincides with advances in econometrics: - from cross-section to panel: Hansen and Tarp (2001) - from OLS to 2SLS and GMM: Michaelowa and Weber (2006); Mishra and Newhouse (2007)… - from a single linear aid regressor to interaction terms and aid subcomponents: Burnside, Dollar (2000); Dalgaard, Hansen, Tarp(2004); Neanidis and Varvarigos (2007)…

6. Empirical strategy (1) Adapt Hansen and Tarp unconditional aid-growth litterature(2001) with 2SLS. Regress per-capita economic growth on aid/GDP and (aid/GDP)², panel:1974-1993 Same controls as Burnside and Dollar(2000) but introduce different instruments: among which one period- lagged value of aid/GDP.

8. Empirical strategy (2) Before giving a unified theory, take and increase BLZ framework with some tests of causal relationship between aid and growth. Expand table 1, larger time-data than HT study: 1962- 2001 (4-year period) replacing HT controls. In particular, he drops the quadratic aid term.

11. Issues of the aid-growth literature (1) Autocorrelation (of the error terms) (2) Instrument proliferation (3) Multicollinearity

12. Context Ideally: instrumentation corrects endogeneity In practice: estimates on aid’s impact present autocorrelation in the errors, there is proliferation of instruments, and multicollinearity These problems bring pessimism on the ability of demonstrating aid effectiveness with cross-country econometrics, thus suggesting that the average effect of aid on growth is too small to be detected statistically

13. (1) Autocorrelation Pooled OLS and 2SLS regression in the aid and growth literature face serial correlation in the errors : if lagged aid is endogenous to lagged growth and the lagged growth innovation is correlated with contemporary growth innovation, then lagged aid can be correlated with it too => possibility of endogeneity bias => lagged variables are not valid instruments

15. (1) Autocorrelation Example: Dropping the lags of aid and aid^2 from the instrument sets in the 2SLS regressions in HT and Clemens, Radelet, and Bhavnani (2004) eliminates the significance of the coefficients on the aid terms => this suggests that identification depends on these instruments

16. (2) Instrument proliferation Diff-in-Diff and GMM estimation, that dominate this literature since the early 2000s, led to instrument proliferation However, the assumptions necessary for the validity of the instruments (Blundell & Bond, 1998) are non-trivial while rarely checked (with difference-in-Hansen tests) to protect the power of the tests => risk of overfitting of endogenous variables

17. (2) Instrument proliferation Example: - in Michaelowa and Weber (2006), the significance of aid terms appears to go hand-in-hand with the instrument count - in Mishra and Newhouse (2007), the coefficient on the lagged dependent variable is 1.0, which invalidates the GMM instruments (Blundell and Bond 1998)

18. (3) Multicollinearity Adding a nearly collinear term to the regression of growth on aid/GDP allows a much better fit by inflating the t stat => 2 collinear aid terms have an inherent propensity to generate seemingly strong results, thus implifying endogeneity bias

19. (3) Multicollinearity Example: - Burnside and Dollar (2000) include both (aid/GDP)*policy and (aid/GDP)^2 *policy in their OLS regression, thus providing huge t stat. But, if one eliminates the collinearity by dropping (aid/GDP)^2 *policy, the large t stat disappear

20. Conclusion (1) The main aid-growth relationship is causal and negative from growth to aid AEL fails in finding significant effect of aid on growth which are robust and free of methodological problems Econometric sophistication has clouded rather than sharpened AEL

21. Conclusion (2) However this does not end the quest of evidence on aid effectiveness but shifts it to smaller questions such as Chen, Mu, and Ravallion (2006) which study how much the placement of WB-financed rural development projects in China can explain sub- national variation in household income ten years later